[2011.01045v2] Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution

Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our performance on the online validation dataset with test time augmentation were as follows: Dice of 0.81, 0.91 and 0.85; Hausdorff (95%) of 20.6, 4,3, 5.7 mm for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mm on the final test dataset, ranking us among the top ten teams. Mor

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@Maxwell_110
@Maxwell_110 2 種の 3D U-Net のアンサンブルによる脳腫瘍 segmentation 📝 t.co/lXzqag5pg2 BraTS2020 のデータを使用しており, - モデル間における loss 設定などの多様性 - auxiliary loss (deep supervision) - SWA 等で,精度の向上を図っている GitHub ➡︎ t.co/taPg3rQxcB t.co/aCkmocEeJJ

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